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Natural scene categorization of images represents a very useful task for automatic image analysis systems in a wide variety of applications. In the literature, several methods have been proposed facing this issue with excellent results. Typically, features of several types are clustered so as to generate a vocabulary able to efficiently represent the considered image collection. This vocabulary is...
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irrelevant parameters are set exactly to zero. Alternatively to standard maximum likelihood estimation (Baum Welch training), in the proposed approach the parameters estimation problem is cast into a Bayesian framework, with the...
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